





"""simple docstring"""
from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


__UpperCamelCase     :						Union[str, Any]						       =  '''pytorch_model.bin'''
__UpperCamelCase     :						Any						       =  '''pytorch_model.bin.index.json'''
__UpperCamelCase     :						Union[str, Any]						       =  '''adapter_config.json'''
__UpperCamelCase     :						Optional[int]						       =  '''adapter_model.bin'''
__UpperCamelCase     :						Dict						       =  '''adapter_model.safetensors'''
__UpperCamelCase     :						List[Any]						       =  '''tf_model.h5'''
__UpperCamelCase     :						Tuple						       =  '''tf_model.h5.index.json'''
__UpperCamelCase     :						List[str]						       =  '''model.ckpt'''
__UpperCamelCase     :						Union[str, Any]						       =  '''flax_model.msgpack'''
__UpperCamelCase     :						Optional[Any]						       =  '''flax_model.msgpack.index.json'''
__UpperCamelCase     :						Optional[Any]						       =  '''model.safetensors'''
__UpperCamelCase     :						List[str]						       =  '''model.safetensors.index.json'''
__UpperCamelCase     :						Any						       =  '''config.json'''
__UpperCamelCase     :						Any						       =  '''preprocessor_config.json'''
__UpperCamelCase     :						int						       =  FEATURE_EXTRACTOR_NAME
__UpperCamelCase     :						Tuple						       =  '''generation_config.json'''
__UpperCamelCase     :						Tuple						       =  '''modelcard.json'''

__UpperCamelCase     :						Optional[int]						       =  '''▁'''
__UpperCamelCase     :						Union[str, Any]						       =  SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

__UpperCamelCase     :						List[Any]						       =  [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
__UpperCamelCase     :						Any						       =  [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
__UpperCamelCase     :						int						       =  [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):




 if version.parse(_lowerCamelCase			) < version.parse(_lowerCamelCase			):
  if "dev" in min_version:
   lowerCAmelCase    				= (
       'This example requires a source install from HuggingFace Transformers (see '
       '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
   )
  else:
   lowerCAmelCase    				= F'This example requires a minimum version of {min_version},'
  error_message += F' but the version found is {__version__}.\n'
  raise ImportError(
      error_message
      + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
      'versions of HuggingFace Transformers.'			)







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						Dict:

	'''simple docstring'''




	def wrapper(*_lowerCamelCase:		Any,					**_lowerCamelCase:		Union[str, Any]			):
		lowerCAmelCase												=  timeit.default_timer()
		lowerCAmelCase												=  func(*_lowerCamelCase,					**_lowerCamelCase			)
		lowerCAmelCase												=  timeit.default_timer() - starttime
		return delta

	lowerCAmelCase												=  func.__name__

	return wrapper




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		List[Any]=100,					_lowerCamelCase:		int=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  seq_shapes or {}
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  {}
		for col_id, (k, v) in enumerate(features.items()			):
			if isinstance(_lowerCamelCase,					_ArrayXD			):
				lowerCAmelCase												=  np.random.rand(*v.shape			).astype(v.dtype			)
			elif isinstance(_lowerCamelCase,					datasets.Value			):
				if v.dtype == "string":
					lowerCAmelCase												=  '''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCAmelCase												=  np.random.randint(10,					size=1			).astype(v.dtype			).item()
			elif isinstance(_lowerCamelCase,					datasets.Sequence			):
				while isinstance(_lowerCamelCase,					datasets.Sequence			):
					lowerCAmelCase												=  v.feature
				lowerCAmelCase												=  seq_shapes[k]
				lowerCAmelCase												=  np.random.rand(*_lowerCamelCase			).astype(v.dtype			)
			lowerCAmelCase												=  data

		dummy_data.append((i, example)			)

	return dummy_data




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any]=100,					_lowerCamelCase:		List[Any]=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  generate_examples(_lowerCamelCase,					num_examples=_lowerCamelCase,					seq_shapes=_lowerCamelCase			)

	with ArrowWriter(features=_lowerCamelCase,					path=_lowerCamelCase			) as writer:
		for key, record in dummy_data:
			lowerCAmelCase												=  features.encode_example(_lowerCamelCase			)
			writer.write(_lowerCamelCase			)

		lowerCAmelCase							,       lowerCAmelCase												=  writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""			)

	lowerCAmelCase												=  datasets.Dataset.from_file(filename=_lowerCamelCase,					info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

	return dataset


